Begin by setting up a Python environment and ensuring you have access to tools like `pip` and `setuptools`, which are necessary for interacting with PyPI. Additionally, set up an Apache Iceberg environment. You might use a local Iceberg installation or a cloud-based setup, depending on your requirements. Ensure you have Hadoop or a compatible file system for Iceberg storage.

Use Python scripts to collect data from PyPI. You can utilize the PyPI JSON API to fetch package metadata. For example, you can use `requests` library in Python to get package information:

```python
import requests

package_name = "example-package"
url = f"https://pypi.org/pypi/{package_name}/json"
response = requests.get(url)
package_data = response.json()
```

Extract relevant metadata, such as version, author, and release date, which you plan to move to Iceberg.

Convert the collected data into a format suitable for Iceberg. Apache Iceberg supports Parquet, Avro, and ORC file formats. Use Python libraries like `pandas` and `pyarrow` to transform JSON data into Parquet or another supported format:

```python
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

df = pd.DataFrame.from_records(package_data['releases'])
table = pa.Table.from_pandas(df)
pq.write_table(table, 'pypi_data.parquet')
```

Ensure the schema of your data aligns with the Iceberg table schema you plan to use.

Set up the Iceberg table to receive the data. Define the schema and partitioning strategy that suits your data access patterns. This is typically done using SQL with a compatible engine like Apache Spark or Presto. For example:

```sql
CREATE TABLE pypi_data (
version STRING,
author STRING,
release_date DATE
) USING iceberg
PARTITIONED BY (release_date);
```

Move the transformed data files (Parquet, Avro, or ORC) into the location where your Iceberg table expects data. This could involve copying files into a specific directory in your Hadoop or cloud storage setup. Ensure the file location and naming conventions match those expected by Iceberg.

Use SQL queries to ingest the data files into the Iceberg table. This might involve using a tool like Apache Hive, Apache Spark, or any compatible SQL engine:

```sql
INSERT INTO pypi_data
SELECT * FROM parquet_file('path/to/pypi_data.parquet');
```

This step ensures that the data is correctly indexed and stored in Iceberg's structured format.

Finally, verify the integrity and correctness of the data loaded into Iceberg. Run queries to check counts, validate schema conformity, and ensure data accuracy:

```sql
SELECT COUNT(*) FROM pypi_data;
SELECT * FROM pypi_data WHERE version = '1.0.0';
```

Ensure that the data aligns with what was extracted from PyPI and that no data loss or corruption occurred during the transfer process. Adjustments can be made as necessary based on these checks.